Paper Number
ECIS2026-1494
Paper Type
CRP
Abstract
Artificial Intelligence (AI) unlocks value-creation opportunities, but its complexity makes it a challenging design material, demanding well-prepared conditions for successful innovation. One reason organisations struggle to materialize AI's potential is initiating AI innovation, which relies on interdisciplinary teams, internal data integration, incremental development, and context information. The Design Challenge, i.e., problem formulation, serves as key artefact guiding AI innovation, but its conceptualisation remains undertheorized leading to a lack of theoretical foundation and practical guidance. Our study conceptualises the Design Challenge as a boundary object enabling interdisciplinary collaboration and serving as a reference point. We performed a qualitative cross-cases analysis of 42 design challenges to surface seven key elements being verb, object of innovation, internal data, technology, target group, context and potential, generating the Design Challenge's anatomy. This advances theoretical understanding of AI innovation and provides a conceptual schema to craft design challenges enhancing the likelihood of impactful AI innovations.
Recommended Citation
Roeckel, Franziska Anna; van Giffen, Benjamin; Janson, Andreas; and Hehn, Jennifer, "The Anatomy Of A Design Challenge For Initiating AI Innovation" (2026). ECIS 2026 Proceedings. 2.
https://aisel.aisnet.org/ecis2026/isd_pm/isd_pm/2
The Anatomy Of A Design Challenge For Initiating AI Innovation
Artificial Intelligence (AI) unlocks value-creation opportunities, but its complexity makes it a challenging design material, demanding well-prepared conditions for successful innovation. One reason organisations struggle to materialize AI's potential is initiating AI innovation, which relies on interdisciplinary teams, internal data integration, incremental development, and context information. The Design Challenge, i.e., problem formulation, serves as key artefact guiding AI innovation, but its conceptualisation remains undertheorized leading to a lack of theoretical foundation and practical guidance. Our study conceptualises the Design Challenge as a boundary object enabling interdisciplinary collaboration and serving as a reference point. We performed a qualitative cross-cases analysis of 42 design challenges to surface seven key elements being verb, object of innovation, internal data, technology, target group, context and potential, generating the Design Challenge's anatomy. This advances theoretical understanding of AI innovation and provides a conceptual schema to craft design challenges enhancing the likelihood of impactful AI innovations.
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